code: def _evaluate_classification(
      self,
      examples: list[ClassificationExample],
      task_type: TaskType,
  ) -> dict[str, Union[Sequence[float], Sequence[int]]]:
      prompts = [
          example.prompt + class_seq
          for example in examples
          for class_seq in example.classes
      ]
      all_logits, all_tokens = self._get_logits_and_tokens(prompts)
      total_logprobs = []
      losses = []
      labels_correct = []
      labels_predicted = []
      prompt_start = 0
      for example in examples:
          n_classes = len(example.classes)
          class_logprobs = []
          for j in range(n_classes):
              class_index = prompt_start + j
              class_logits = all_logits[class_index]
              # the lengths of each class sequence in tokens
              class_sequence = example.classes[j]
              # NOTE: we subtract 1 if OPT because the first token is the
              # start of the sequence
              target_token_length = (
                  len(self.tokenizer(class_sequence)["input_ids"])
                  - self.correction_for_start_token
              )
              # we only need the logits for the end sequence
              tokens = all_tokens[class_index]
              # we have to go back by one because we don't care about the
              # logits for the predicted token
              sequence_logits = class_logits[
                  -target_token_length - 1 : -1
              ]
              sequence_tokens = tokens[-target_token_length:]
              # we take a log_softmax over all token logits for each position in the class sequence to
              # get log probabilities, and then sum the logprobs for the
              # tokens actually chosen
              logprobs = F.log_softmax(sequence_logits, dim=-1)
              class_logprob = sum(
                  [
                      logprobs[i, token]
                      for i, token in enumerate(sequence_tokens)
                  ]
              )
              # type: ignore (the sum is never empty so never just 0, always
              # a tensor)
              class_logprobs.append(class_logprob.item())
  
          total_logprob = torch.logsumexp(
              torch.tensor(class_logprobs), dim=-1
          ).item()
          normalised_logprobs = F.log_softmax(
              torch.tensor(class_logprobs), dim=-1
          )
          loss = -normalised_logprobs[example.answer_index].item()
          label_correct = int(
              np.argmax(normalised_logprobs) == example.answer_index
          )
          total_logprobs.append(total_logprob)
          losses.append(loss)
          labels_correct.append(label_correct)
  
          label_predicted = example.classes[
              torch.tensor(class_logprobs).argmax(dim=-1).item()
          ]
          labels_predicted.append(label_predicted)
  
          prompt_start += n_classes
      return {
          "loss": losses,
          "correct": labels_correct,
          "predicted": labels_predicted,
          "total_logprob": total_logprobs,
      }
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Union, cast, Sequence
  from typing_extensions import Literal, get_args
  import numpy
  import torch
  import torch.nn.functional
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from accelerate import Accelerator
  from .isp_dataset import ClassificationExample, Example, ExampleWithClasses, LogoddsExample, SequenceProbExample, NumericExample, TaskType
  from .isp_utils import BasicParser
  from .isp_utils import OpenAIModel, OpenaAIModelList
label_desc: Use image node_id 4 for calling the HFModel obj's underlying member method code with example usage: obj._evaluate_classification(examples, task_type) and returns: dict